Abstract
In this paper, the problem of colour image segmentation is addressed using the Dempster–Shafer (DS) theory. Examples are provided showing that this theory is able to take into account a large variety of special situations that occur and which are not well solved using classical approaches. Modelling both uncertainty and imprecision, and computing the conflict between images and introducing a priori information are the main features of this theory. Consequently, the performance of such a segmentation scheme is largely conditioned by the appropriate estimation of mass functions in the DS evidence theory. In this paper, a new method of automatically determining the mass function for colour-image segmentation problems is presented. The mass function of each pixel is determined by applying possibilistic c-means (PCM) clustering to the grey levels of the three primitive colours. A reliability criterion, associated with each pixel and the mass functions of its neighbouring pixels, is used into a fuzzy based reasoning system in order to decide on the appropriate segmentation. Experimental segmentation results on medical and textured colour images highlight the effectiveness of the proposed method.
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Ben Chaabane, S., Sayadi, M., Fnaiech, F. et al. A New Method for the Estimation of Mass Functions in the Dempster–Shafer’s Evidence Theory: Application to Colour Image Segmentation. Circuits Syst Signal Process 30, 55–71 (2011). https://doi.org/10.1007/s00034-010-9207-3
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DOI: https://doi.org/10.1007/s00034-010-9207-3